AutoPentest-DRL

AutoPentest-DRL: Automated Penetration Testing Using Deep Reinforcement Learning AutoPentest-DRL is an automated penetration testing framework based on Deep Reinforcement Learning DRL techniques. AutoPentest-DRL can determine the most appropriate attack path for a given logical network, and can...

Finding Software Supply Chain Attack Paths with Logical Attack Graphs

Cyberattacks are becoming increasingly frequent and sophisticated, often exploiting the software supply chain SSC as an attack vector. Attack graphs provide a detailed representation of the sequence of events and vulnerabilities that could lead to a successful security breach in a system. MulVal ...

AutoPentest-DRL - Automated Penetration Testing Using Deep Reinforcement Learning

AutoPentest-DRL is an automated penetration testing framework based on Deep Reinforcement Learning DRL techniques. The framework determines the most appropriate attack path for a given network, and can be used to execute a simulated attack on that network via penetration testing tools, such as...